International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Cost-Effective Resource Management in Cloud Computing using AIBased Forecasting Mohammad Shahbaz1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract
- The expansion of cloud computing exponentially has ensured that cloud has become an essential service in the digital space. Nonetheless, costeffective and efficient control over the resources is an ongoing problem since the cloud workloads are dynamic and unpredictable. The poor resource allocation methods that include traditional provides like static provisioning and rule based systems, usually result to over-provisioning, under-utilization and high costs of operations. The current study is based on several shortcomings; hence, to overcome them, this study suggests an Artificial Intelligence (AI) powered forecasting model of proactive and adaptive cloud resource management.
businesses and people to develop flexible and cost effective IT infrastructures, and this can be made possible without any major upfront expense investing on hardware. As much as this has very amazing advantages, the management of resources in the cloud is one of the core challenges in the cloud environment especially with regards to the fact that the workload is more dynamic and unpredictable. This study deals with this issue by examining the opportunities of using artificial intelligence (AI), particularly, the AI-based forecasting models, in order to get the best cloud resource management approach.
1.1. Background of Cloud Computing
In the study, they investigate machine learning (ML) and deep learning (DL) algorithms (Random Forest, Gradient Boosting, ARIMA and Long Short-Term Memory (LSTM) networks, etc.) that supposedly could be used to forecast the future needs in the resources foresight of the historical data on workload. These forecasts are combined in an adaptive framework to manage resources which make allocation decisions in real-time as optimally possible with regard to both performance and cost.
The cloud computing allows access to shared pools of configurable computing resources on demand over the internet and allows much broader set of application and services supported. The use of clouds in the healthcare, financial sectors, educational, and web-based businesses is facilitated by scalability, flexibility, and cost saving characteristics of the cloud technology. Infrastructure-asa-service (IaaS) model, especially, provides virtualized resources to the user which they can expand or reduce on the basis of their need hence it is the best option when it comes to dynamic load. Nevertheless, the growing complexity of cloud environments has raised serious problems in the efficient management of such resources and particularly, when dealing with the constantly changing demand.
Its evaluation is done in the simulated and real cloud environments with available datasets including Google Cluster Trace and Azure Public Dataset. The major performance indicators should be the accuracy in forecasting, efficient use of resources, level of cost savings, and the performance of system in different levels of workloads. The findings of the conducted experiment indicate that the proposed AI-based strategy is much better than methods of the traditional types in terms of the optimal resource consumption and low operational expenses. Key Words: Cloud Computing, Resource Management, AIBased Forecasting, Cost Optimization, Machine Learning, Deep Learning, Workload Prediction, Dynamic Resource Allocation.
1. INTRODUCTION Cloud computing has transformed how services can reach computing services and give scalable and on-demand computing resources like storage, network and processing capabilities. It has provided the opportunity of allowing
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Figure-1: Cloud Computing.
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